Questões
Questão 1
MRT_1F <-c(517.1468515630205, 85.13094142168089, 30.333207896694553, 12.694776264558937, 3.3041601673945418, 1.1823111717498882, 1.1892293502386786)
MRT_3F <-c(156.68929936163462, 11.540837783562276, 0.4512835621696538, 0.4509797929766453, 0.4502068233039181, 0.4496185276300172, 0.4543157082191288)
MRT_5F <-c(83.90319666471157, 0.3068151086494968, 0.30522314133037304, 0.3072588968084928, 0.30655265997285697, 0.3055812715727718, 0.3053297166713006)
MRT_10F <-c(29.55430642951759, 0.19832832665772515, 0.1971923924717474, 0.19796648905716516, 0.19615594370806338, 0.2034569237883263, 0.19617420889447737)
MRT_15F <-c(11.317736530583566, 0.167364215666193, 0.16172168266811013, 0.16701085329580515, 0.1598052657153692, 0.1645934043532696, 0.16216563797118075)
MRT_sem_F <-c(11.93430909937736, 0.6095414637034009, 0.6060645101029295, 0.612167181646899, 0.6146761002685637, 0.6096747087200697, 0.6125810476877268)
clock <- c(0.1, 0.5, 1, 1.5, 2, 2.5, 3)
par(mar = c(5, 4, 4, 4) + 0.1)
plot(clock, MRT_1F, col = "black",type = "o", ylim = range(MRT_1F, MRT_3F, MRT_5F, MRT_10F, MRT_15F, MRT_sem_F),
xlab = "Time between Things Request (seconds)",
ylab = "Response Time (sec.)", main = "Gráfico de Linhas", pch = 4)
lines(clock, MRT_3F, col = "yellow", type = "o", pch = 11)
lines(clock, MRT_5F, col = "red", type = "o", pch = 1)
lines(clock, MRT_10F, col = "blue", type = "o", pch = 2)
lines(clock, MRT_15F, col = "purple", type = "o", pch = 5)
lines(clock, MRT_sem_F, col = "green", type = "o", pch = 4)
legend("topright", legend = c("MRT_1F", "MRT_3F", "MRT_5F", "MRT_10F", "MRT_15F", "MRT_sem_F"),
col = c("black", "yellow", "red", "blue", "purple", "green"), lty = 1, pch = c(4, 11, 1, 2, 5, 4))

layout(matrix(1:6, nrow = 2, byrow = TRUE))
data_matrix <- rbind(MRT_sem_F, MRT_1F)
data_matrix <- log(data_matrix)
colors <- c("#E6E6E6", "#666666")
barplot(data_matrix, beside = TRUE, col = colors,
main = "1 Fog", xlab = "Time between Things requests", ylab = "Response time (s)",
names.arg = clock)
legend("topright", legend = c("MRT_sem_F", "MRT_1F"), fill = colors)
data_matrix <- rbind(MRT_sem_F, MRT_3F)
data_matrix <- log(data_matrix)
barplot(data_matrix, beside = TRUE, col = colors,
main = "3 Fog", xlab = "Time between Things requests", ylab = "Response time (s)",
names.arg = clock)
legend("topright", legend = c("MRT_sem_F", "MRT_3F"), fill = colors)
data_matrix <- rbind(MRT_sem_F, MRT_5F)
data_matrix <- log(data_matrix)
barplot(data_matrix, beside = TRUE, col = colors,
main = "5 Fog", xlab = "Time between Things requests", ylab = "Response time (s)",
names.arg = clock)
legend("topright", legend = c("MRT_sem_F", "MRT_5F"), fill = colors)
data_matrix <- rbind(MRT_sem_F, MRT_10F)
data_matrix <- log(data_matrix)
barplot(data_matrix, beside = TRUE, col = colors,
main = "10 Fog", xlab = "Time between Things requests", ylab = "Response time (s)",
names.arg = clock)
legend("topright", legend = c("MRT_sem_F", "MRT_10F"), fill = colors)
data_matrix <- rbind(MRT_sem_F, MRT_15F)
data_matrix <- log(data_matrix)
barplot(data_matrix, beside = TRUE, col = colors,
main = "15 Fog", xlab = "Time between Things requests", ylab = "Response time (s)",
names.arg = clock)
legend("topright", legend = c("MRT_sem_F", "MRT_15F"), fill = colors)

Questão 2
meal_price <- c("$10-19", "$20-29", "$30-39", "$40-49")
Good <- c(53.8, 33.9, 2.6, 0.0)
Very_Good <- c(43.6, 54.2, 60.5, 21.4)
Excellent <- c(2.6, 11.9, 36.8, 78.6)
data_matrix <- rbind(Good, Very_Good, Excellent)
colors <- c("green", "lightblue", "blue")
barplot(data_matrix, beside = FALSE, col = colors,
main = "Quality Rating by Meal Price", xlab = "Meal Price ($)", ylab = "Percentage (%)",
names.arg = meal_price)
legend("topright", legend = c("Good", "Very Good", "Excellent"), fill = colors)

Questão 3
data("airquality")
airquality <- airquality[airquality$Month == 5, ]
airquality$Temp <- (airquality$Temp-32)/1.8
hist(airquality$Temp, main = "Histograma das Temperaturas em Maio (°C)",
xlab = "Temperatura (°C)",
ylab = "Frequência",
col = "lightblue",
border = "black",
prob = TRUE, )
lines(density(airquality$Temp), col = "red")

Questão 4
sales <- read.table("https://training-course-material.com/images/8/8f/Sales.txt",header=TRUE)
paises <- sales$COUNTRY
vendas <- sales$SALES
vendas_perc <- vendas / sum(vendas) * 100
labels <- paste(vendas, " (", round(vendas_perc, 1), "%)", sep="")
colors <- c("blue", "lightblue", "lightgreen", "orange", "yellow", "red")
pie(vendas_perc, labels = labels, col = colors,
main = "Total e Percentual de Vendas por País")
legend("topleft", legend = paises, fill = colors)

Questão 5
data(InsectSprays)
boxplot(count ~ spray, data = InsectSprays,
outline = FALSE,
main = "Gráfico BoxPlot - Quantidade de Spray por Tipo",
xlab = "Tipos de Spray",
ylab = "Quantidade",
col = "yellow"
)

Questão 6
dados_NONE <- read.csv("monitoringCloudData_NONE.csv")
dados_0.1 <- read.csv("monitoringCloudData_0.1.csv")
dados_0.5 <- read.csv("monitoringCloudData_0.5.csv")
dados_1 <- read.csv("monitoringCloudData_1.csv")
dados_NONE <- dados_NONE[, c("currentTime", "usedMemory")]
dados_0.1 <- dados_0.1[, c("currentTime", "usedMemory")]
dados_0.5 <- dados_0.5[, c("currentTime", "usedMemory")]
dados_1 <- dados_1[, c("currentTime", "usedMemory")]
ajustar_dados <- function(dados) {
dados$currentTime <- strptime(dados$currentTime, "%Y-%m-%d %H:%M:%S")
dados$currentTime <- difftime(dados$currentTime, dados$currentTime[1], units = "hours")
dados$usedMemory_unidade_orig <- gsub("[0-9.]", "", dados$usedMemory)
dados$usedMemory_orig <- as.numeric(gsub("[^0-9.]", "", dados$usedMemory))
dados$usedMemory <- ifelse(dados$usedMemory_unidade_orig == "TB", dados$usedMemory_orig * 1000000,
ifelse(dados$usedMemory_unidade_orig == "GB", dados$usedMemory_orig * 1024,
dados$usedMemory_orig))
return(dados)
}
dados_NONE <- ajustar_dados(dados_NONE)
dados_0.1 <- ajustar_dados(dados_0.1)
dados_0.5 <- ajustar_dados(dados_0.5)
dados_1 <- ajustar_dados(dados_1)
layout(matrix(1:4, nrow = 2, byrow = TRUE))
plot(dados_NONE$currentTime, dados_NONE$usedMemory, type = "l", col = "black", xlab = "Time (hour)", ylab = "Used Memory (MB)")
plot(dados_0.1$currentTime, dados_0.1$usedMemory, type = "l", col = "black", xlab = "Time (hour)", ylab = "Used Memory (MB)")
plot(dados_0.5$currentTime, dados_0.5$usedMemory, type = "l", col = "black", xlab = "Time (hour)", ylab = "Used Memory (MB)")
plot(dados_1$currentTime, dados_1$usedMemory, type = "l", col = "black", xlab = "Time (hour)", ylab = "Used Memory (MB)")

Questão 7
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(plotly)
## Carregando pacotes exigidos: ggplot2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
dados <- read.csv("netflix_titles.csv")
dados_filtrados <- dados %>%
filter(!is.na(country) & country != "") %>%
filter(!grepl(",", country))
conteudos_por_pais <- dados_filtrados %>%
count(country) %>%
arrange(desc(n)) %>%
slice(1:10)
plot_ly(conteudos_por_pais, labels = ~country, values = ~n, type = 'pie') %>%
layout(title = 'Top 10 países com mais conteúdo na Netflix',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
Questão 8
df <- data.frame(conteudos_por_pais)
colnames(df) <- c("País", "Total de conteúdos")
pais <- as.list(df$País)
total_conteudos <- as.list(df$`Total de conteúdos`)
plot_ly(
type = "table",
header = list(
values = names(df),
fill = list(color = "gray"),
font = list(color = "white"),
align = "center"
),
cells = list(
values = list(pais, total_conteudos),
align = "center"
)
)
Questão 9
dados$decada <- as.numeric(substr(dados$release_year, 1, 3)) * 10
dados_por_decada <- dados %>%
filter(!is.na(release_year)) %>%
group_by(decada, type) %>%
summarise(contagem = n()) %>%
ungroup()
## `summarise()` has grouped output by 'decada'. You can override using the
## `.groups` argument.
plot_ly(dados_por_decada, x = ~decada, y = ~contagem, color = ~type,
type = 'scatter', mode = 'lines+markers', colors = c('yellow', 'blue')) %>%
layout(#title = 'Quantidade de Conteúdo por Década na Netflix',
xaxis = list(title = 'Década'),
yaxis = list(title = 'Qnd. Conteúdo'),
legend = list(title = 'Tipo de Conteúdo',
x = 1, y = 0.95))
Questão 10
filmes20002010 <- dados %>%
filter(type == "Movie", release_year > 2000, release_year < 2010)
filmes20002010$primeiro_genero <- sub(",.*", "", filmes20002010$listed_in)
filmes20002010$primeiro_genero <- factor(filmes20002010$primeiro_genero,
levels = c("Dramas", "Action & Adventure", "Comedies"))
filmes20002010$primeiro_genero <- recode(filmes20002010$primeiro_genero, "Dramas" = "Drama", "Action & Adventure" = "Ação e Aventura", "Comedies" = "Comédia")
dados_agrupados <- filmes20002010 %>%
group_by(release_year, primeiro_genero) %>%
summarise(contagem = n()) %>%
filter(primeiro_genero %in% c("Drama", "Ação e Aventura", "Comédia")) %>%
ungroup()
## `summarise()` has grouped output by 'release_year'. You can override using the
## `.groups` argument.
plot_ly(dados_agrupados, x = ~release_year, y = ~contagem, color = ~primeiro_genero,
type = 'bar', width = 0.5) %>%
layout(title = 'Quantidade de Filmes por Gênero entre 2000 e 2010',
xaxis = list(title = 'Ano de Lançamento'),
yaxis = list(title = 'Qnt. de Lançamentos'),
barmode = 'group',
legend = list(title = 'Gênero'))